Update app.py

app.py

@@ -94,7 +94,8 @@ def prod_function(transformer_model, prod_dl, prod_data):
     accelerated_model.eval()
 
     # Find Embedding of the image to be evaluated
-    emb_prod = accelerated_model(acclerated_prod_data)
+    with torch.no_grad():
+        emb_prod = accelerated_model(**acclerated_prod_data)
 
     prod_preds = []
 
@@ -186,8 +187,8 @@ prod_ds = dataset_prod_obj.create_dataset(image_paths, webcam=False)
 prod_dl = DataLoader(prod_ds, batch_size=BATCH_SIZE)
 
 # Testing the dataloader
-#prod_inputs = next(iter(prod_dl))
-#st.write(prod_inputs['pixel_values'].shape) 
+prod_inputs = next(iter(prod_dl))
+st.write(prod_inputs['pixel_values'].shape) 
 
 # Read image from Camera
 enable = st.checkbox("Enable camera")
@@ -205,13 +206,13 @@ if picture is not None:
     prod_inputs = next(iter(webcam_dl))
     st.write(prod_inputs['pixel_values'].shape)
     
-    ## Run the predictions
-    #prediction = prod_function(model_pretrained, prod_dl, webcam_dl)
-    #predictions = torch.cat(prediction, 0).to(device)
-    #match_idx = torch.argmin(predictions)
-
-    ## Display the results
-    #if predictions[match_idx] <= 0.3:
-    #  st.write('Welcome: ',image_paths[match_idx].split('/')[-1].split('.')[0])
-    #else:
-    #  st.write("Match not found")
+    # Run the predictions
+    prediction = prod_function(model_pretrained, prod_dl, webcam_dl)
+    predictions = torch.cat(prediction, 0).to(device)
+    match_idx = torch.argmin(predictions)
+
+    # Display the results
+    if predictions[match_idx] <= 0.3:
+      st.write('Welcome: ',image_paths[match_idx].split('/')[-1].split('.')[0])
+    else:
+      st.write("Match not found")